TL;DR:
- AI visibility tools give you the diagnostic data to see where your brand is missing from LLM-generated answers. The tool alone does not fix the problem. This guide evaluates Profound, Peec, Scrunch, and Trysight across pricing, citation fidelity, CRM integration, and team fit.
- Data without execution is overhead: These platforms show you the gaps. Closing them requires structured content built for passage retrieval.
- Search has split into three surfaces: Web rankings, AI citations, and training data now require distinct tactical priorities.
- Information consistency beats link count: LLMs weight claims that appear consistently across independent sources, which changes what off-page strategy looks like.
- Entry software cost ranges from ~€89 to ~$400/month, covering monitoring only. Budget separately for content execution, which is where citation rates actually move.
Buyers complete a significant portion of their vendor research inside ChatGPT, Claude, and Perplexity before they ever visit a website. If your brand is absent from those answers, you lose pipeline your attribution stack never sees. This guide evaluates four AI visibility platforms based on hands-on testing, so you can choose the right diagnostic stack and understand what it takes to turn those diagnostics into measurable pipeline.
Defining AI visibility for SaaS growth
AI visibility refers to your brand's measurable presence inside LLM-generated answers, tracked across citation rate, mention rate, and share of voice. An AI visibility platform monitors those mentions across answer engines, benchmarks your performance against competitors, and surfaces content gaps. These tools are diagnostic, not prescriptive. They show you where you are losing, not how to stop losing.
Modern organic search operates across three distinct surface areas: web search (traditional rankings), citations (LLM-retrieved passages), and training data (associations baked into model weights). Most AI visibility tools focus on citations, where the most actionable short-term work lives. Understanding all three helps you prioritize spend and effort correctly.
The shift from search clicks to AI citations
Classic SEO produced a ranked list of URLs. A buyer clicked one. You tracked the session. Answer engines like ChatGPT, Google AI Overviews, Perplexity, and Claude now synthesize a single answer from semantically relevant passages retrieved across multiple sources, often without any click. Google scores documents and returns ranked URLs. LLMs retrieve passages that match the semantic intent of a query and stitch them into a coherent response.
Research we track from Ahrefs shows that top-10 Google rankings accounted for 76% of AI Overview citations in mid-2025, but only 38% by early 2026. Traditional SEO metrics do not capture this divergence. For more on why SEO and AEO require different tactical priorities, I covered this in detail in this SEO vs. AEO breakdown.
KPIs for tracking AI search impact
The metrics that matter for AI visibility differ from traditional click and impression counts. The core KPIs to track include:
- Citation rate: The percentage of category queries where your brand appears in an LLM-generated answer.
- Share of voice: Your brand's citation count versus total citations across your competitive set for a defined query pool.
- Mention quality: Whether citations are positive, neutral, or negative, and whether they reference accurate product information.
- Brand recognition score: How prominently and accurately your brand is described relative to competitors inside LLM responses.
An Answer Engine Optimization (AEO) Score aggregates these signals, measuring how effectively your content answers specific user queries compared to competitors across the engines you track. These tools measure that score. Shifting it requires content production, not just monitoring.
Criteria used to rank top AI visibility systems
This evaluation is based on hands-on testing and analysis across multiple platforms. The shortlist of four was determined by how well each tool serves B2B SaaS marketing teams with a real pipeline attribution need. Tools built primarily for consumer brand monitoring or those without verifiable pricing were excluded.
Four criteria drove the shortlist: citation fidelity (does the tool reflect real-time LLM outputs, not cached approximations), integration depth (can it connect to Salesforce, HubSpot, or GA4), pricing transparency (is pricing public and structured for SaaS teams), and speed to signal (how quickly does baseline data appear after setup).
How we measure citation fidelity
Citation fidelity is the most important evaluation criterion and the hardest to verify independently. It measures whether a tool's reported citations reflect what an LLM actually outputs under real buyer conditions. We published a detailed analysis of AI tracking platform flaws covering a methodological issue in how tools run queries. When evaluating a platform, ask to see a live query demonstration and confirm how the platform accounts for output variance.
B2B SaaS marketing teams typically involve an agency partner, an in-house content lead, and a demand generation manager all needing access to the same data at different permission levels. Peec AI reportedly offers unlimited seats on all paid plans, a meaningful advantage for shared workflows. Profound reportedly lacks multi-account management, which constrains agency use cases. Scrunch reportedly supports agency accounts under a separate partner program tier. Based on available information, none of the four platforms appear to offer deep native CRM connectors out of the box. Peec reportedly provides API access on Enterprise plans (in beta). Profound reportedly offers API access for custom HubSpot or Salesforce builds. Scrunch and Trysight reportedly require Zapier or developer-built API setups for CRM integration.
Budgeting for AI visibility services
Software subscription costs and execution costs are separate budget lines. The tools here range from $89/month for Peec's Starter plan to $399/month for Profound's Growth plan. These costs cover monitoring and diagnostics only. They do not cover content production, schema implementation, off-page consistency work, or technical optimization.
We start at €6,995/month and include up to 20 CITABLE-framework articles, a dedicated team of four, AI visibility tracking, competitor monitoring, structured data, backlink and brand consistency work, and strategic Reddit engagement. The in-house vs. agency cost comparison is worth modeling carefully before you decide which path fits your stage. For a full payback model, see our AEO ROI calculator for B2B SaaS.
Time to first AI citation
Most platforms populate a baseline visibility report within 24 to 48 hours of connecting your domain. That report shows your current citation rate, not what it should be. Based on our client work, initial citation signals from new content appear within one to two weeks of publishing CITABLE-structured pieces. Material citation rate lift takes three to four months. Measurable pipeline contribution typically emerges by month three, as attribution tags accumulate enough data volume to support a credible board narrative.
Profound: analyzing brand citations and impact
Profound tracks brand mentions across ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, and Microsoft Copilot. It offers one of the broadest LLM coverage sets on the market and suits enterprise SaaS teams that treat AI visibility as a priority channel. Pricing reportedly starts at approximately $99/month for the Starter plan (50 tracked prompts, ChatGPT coverage). The Growth plan reportedly runs around $399/month with 100 prompts across three answer engines, or approximately $332.50/month on annual billing.
Beyond mention tracking, Profound provides competitor benchmarking and optimization recommendations. The breadth of platform coverage is a genuine strength for teams that need visibility across all major answer engines in a single dashboard.
Profound's strength is in identifying citation gaps at scale. You can map your brand's presence against specific buyer-intent queries, see which competitors appear in your place, and prioritize content gaps by query volume. In our work with incident.io, systematic tracking and optimization pulled AI visibility from 38% to 64%, closing the competitive gap against PagerDuty. That lift starts with a clear baseline from a diagnostic tool. You can review the full methodology in the incident.io case study.
Known gaps in visibility coverage
Profound's primary limitations are cost and configurability. At approximately $399/month for the Growth plan, which caps you at three platforms (ChatGPT, Perplexity, and Google AI Overviews), it is priced higher than some competitors offering broader LLM coverage at lower price points. This makes justification harder for teams below Series B. The platform does not support multi-account management, ruling it out for agencies or SaaS companies working with an external partner who needs shared access. CDN-dependent attribution reportedly creates blind spots for some SaaS delivery architectures. Customizing prompt sets or weighting queries by pipeline value is limited compared to enterprise expectations.
Ideal use cases for Profound
Profound is best for enterprise SaaS teams with a large existing content library, a dedicated SEO or demand generation manager, and a need to benchmark across multiple LLMs simultaneously. If your team is asking which specific queries you are losing to competitors, Profound gives you the most granular answer of the four platforms evaluated.
Peec: strategic fit for B2B SaaS pipelines
Peec AI tracks brand mentions across ChatGPT, Perplexity, and Google AI Overviews, focusing on commercial buyer-intent queries rather than broad informational coverage. The Starter plan reportedly runs approximately €89/month with tracked prompts and unlimited seats. The Pro plan is reportedly around €199/month with 100 prompts, and the Enterprise plan is reportedly around €499/month with 300 prompts. Unlimited seats across all tiers makes it accessible for agencies and in-house teams sharing a single account.
Peec's differentiated approach reportedly uses UI scraping technology that simulates real user interactions rather than querying APIs directly, which produces outputs closer to actual buyer query behavior. However, the platform reportedly does not offer built-in pipeline attribution and has no native CRM integration. API access is reportedly available for Enterprise customers but remains in beta.
Proven impact on visibility
Peec's commercial query focus maps directly to pipeline because it surfaces the specific questions buyers ask before requesting a demo. By identifying query gaps at the commercial intent layer, you can prioritize content production around the searches that convert. We used systematic citation tracking to support a B2B SaaS client who grew AI-referred trials from 550 to 3,500+ in seven weeks by identifying the exact query categories where the brand was absent and producing targeted content accordingly.
Where Peec falls short
Peec's core limitation is that it reportedly stops at the diagnostic layer. There are reportedly no content tools, no optimization engine, no technical audit capabilities, and no traditional SEO context layered into its recommendations. A team using Peec gets accurate data on where they are losing, but no structured path to fixing it. Without a clear content execution plan, the subscription produces reports that trigger internal debate rather than action. End-to-end pipeline attribution from citation to closed revenue is reportedly not available natively on the platform.
Ideal use cases for Peec
Peec suits demand generation-focused teams at Series A or B SaaS companies with a content team ready to act on the data. It is also the strongest fit for teams that need to share access across multiple users without per-seat licensing friction. If you already have a clear content production system and need a lightweight, affordable citation tracker to feed it, Peec is a strong starting point.
Analyzing Scrunch for AI search visibility
Scrunch AI monitors brand visibility across seven or more answer engines including ChatGPT, Gemini, Perplexity, Claude, Meta AI, Google AI Mode, and Google AI Overviews. Its prompt and citation analytics tracks which queries generate brand mentions, how frequently your content is cited, and which competitors appear alongside you. The Core plan reportedly starts at $250/month, with Agency Core at $500/month. Enterprise and Agency Enterprise pricing is reportedly custom. A 7-day free trial of the Core plan is reportedly available.
Scrunch's configurable monitoring and extensive platform coverage suit teams that need visibility across both mainstream LLMs and newer AI surfaces in a single interface.
Core AI visibility capabilities
Scrunch tracks brand mentions across all major AI engines and allows user-configurable prompt sets, giving marketing teams control over which buyer questions they monitor. Our analysis found that Reddit occupies a significant share of ChatGPT's internal search activity during query processing despite a much lower share of visible citations. Teams that monitor community and discussion signals alongside direct LLM citations get a fuller picture of what shapes AI recommendations. I covered this Reddit dynamic in detail in this Reddit strategy video.
Known constraints and tradeoffs
Scrunch converts keywords into prompts rather than tracking actual user queries as they occur in the wild. The platform's data reflects your configured query set, not live buyer behavior. Actionable optimization guidance beyond monitoring is limited, and Scrunch's AI Experience Platform was reportedly still in limited testing during our evaluation period. The reportedly $250/month entry price is also higher than Peec for teams that only need basic citation tracking without full multi-engine coverage.
Ideal use cases for Scrunch
Scrunch is best suited for enterprise SaaS companies and agencies that need broad multi-platform monitoring with configurable prompt tracking. It is a strong fit for teams managing multiple brands under the Agency Partner Program tier, or organizations with compliance requirements that need documented visibility across every major AI engine simultaneously.
Trysight reportedly tracks brand visibility across AI search platforms including ChatGPT, Google AI Overviews, Perplexity, and Claude, with a focus on real-time citation monitoring and competitive benchmarking. The platform publishes extensive guidance on AI visibility strategy, pricing comparisons, and tool evaluations alongside its monitoring capabilities. Trysight's pricing is reportedly not publicly listed and requires a direct conversation with their team to scope. This makes it harder to compare directly at the selection stage, though the platform is actively deployed by SaaS marketing teams.
Trysight's content coverage of Google AI Overview changes and competitive conquesting patterns is detailed and practically useful for teams building an internal understanding of AI search before committing to a monitoring stack.
Why Trysight drives AI visibility
Trysight's monitoring capability focuses on detecting citation changes and competitive displacement inside AI-generated answers, particularly Google AI Overviews. For SaaS companies whose pipeline depends on maintaining visibility inside Google's synthesized answers, fast detection of competitor displacement is a genuine operational need. The platform also covers the educational layer of AI visibility strategy, which helps marketing teams build internal alignment on what metrics to track before setting up CRM attribution. For a full operational sequence beyond the tool itself, I covered the exact steps in this 2026 AEO strategy walkthrough.
Key implementation hurdles
The main implementation challenge with Trysight is the absence of publicly listed pricing. Technical marketing teams cannot accurately scope an integration project without direct engagement with the Trysight team to define data architecture and CRM connection requirements. Budget planning is also harder when pricing requires a sales conversation. Teams that need to present a firm software line item before starting procurement will find the other three platforms easier to evaluate at speed.
Trysight is most appropriate for technical marketing teams that value detailed AI Overview monitoring and are comfortable engaging directly with the vendor to scope pricing and integration. For teams that need a documented, self-serve monitoring platform from day one with public pricing, Profound, Peec, or Scrunch are lower-friction starting points.
Here is a direct comparison of the four platforms across the criteria that matter most for B2B SaaS marketing teams.
Platform | Monthly Price (Entry) | Key Strength | CRM Integration | Best Team Profile |
|---|
Profound | $99 (Starter), $399 (Growth) | Multi-LLM coverage breadth | API-based (custom build) | Enterprise SaaS, large content library |
Peec AI | €89 (Starter), €199 (Pro) | Unlimited seats, commercial query focus | API on Enterprise (beta) | Demand gen teams, Series A-B |
Scrunch | $250 (Core), $500 (Agency Core) | Multi-engine configurable monitoring | Zapier or custom API | Enterprise, agencies, multi-brand |
Trysight | Custom (contact sales) | AI Overview monitoring and strategy | Developer-required setup | Technical in-house teams |
Verifying AI citation accuracy
AI hallucination is a risk inside these platforms, not just in the LLM outputs they track. LLMs sometimes invent pricing, fabricate features, or attribute quotes to the wrong company. A monitoring platform that does not detect these inaccuracies gives you a false sense of visibility quality. Factor this into how you present data internally.
Mapping these tools to B2B SaaS CMO decision criteria produces a clear picture. Peec wins on pricing transparency and seat flexibility. Scrunch adds multi-engine coverage that others limit. Trysight adds AI Overview depth but requires direct vendor engagement to scope. Based on available information, none of the four tools appear to provide content production, technical optimization, or off-page consistency work. All four reportedly require an execution partner or in-house capability to move the metrics they report. For how to evaluate that execution partner, see our B2B SaaS SEO agency evaluation framework.
Annual software subscription costs reportedly range from approximately €1,068/year for Peec Starter to around $3,990/year for Profound Growth on annual billing. Scrunch Core reportedly runs approximately $3,000/year on annual billing. These budgets cover dashboards and reports, not the content, schema, or off-page work that changes the numbers. Our B2B SaaS SEO agency pricing breakdown covers the full cost model. Treating monitoring costs as the primary budget decision leads to paying for increasingly precise data about a problem you have no execution plan to fix.
Time to first AI mention
Peec and Profound reportedly populate baseline reports within 24 to 48 hours of setup. Scrunch's setup timeline depends on the query set you configure and the number of engines you activate. Trysight's setup timeline varies based on the scope defined with their team. For new content you publish and want to see cited, the realistic detection window across all platforms is one to two weeks after a piece has been indexed and retrieved in LLM outputs.
Choosing the right tool depends on three variables: team size and internal capability, budget available for software versus execution, and existing CRM infrastructure. The decision tree below maps these variables to a recommended starting point.
Strategies for sub-10 person teams
For small marketing teams, setup time and seat cost matter as much as feature depth. Peec's reportedly €89 Starter plan with unlimited seats means your entire team and any agency partner can access the same data without additional cost. Start with 25 high-intent commercial queries mapped to your primary use case, connect the citation data to a simple HubSpot workflow, and use the output to prioritize which content assets to restructure first. Run our free AEO content evaluator against your top five assets to identify immediate extractability gaps.
Scaling AI search for 10-50 person teams
Mid-sized teams benefit from deeper integration between citation data and pipeline tracking. Options like Peec Pro (reportedly around €199/month) or Scrunch Core (reportedly around $250/month) may make sense at this stage depending on whether your category has significant multi-engine exposure. Connect citation data to HubSpot or Salesforce using the platforms' API capabilities or Zapier. Apply the CITABLE framework across your top-performing content assets first, starting with pages that already rank in the top 10 for high-intent queries. For the full operational sequence, see our 2026 AEO strategy walkthrough.
Scaling AI strategy for enterprise SaaS
Enterprise SaaS companies with large content libraries and multiple competing categories should deploy Profound for its multi-LLM coverage breadth. Set up custom Salesforce fields to capture AI-referred Marketing Qualified Lead (MQL) sources, implement a self-reported "how did you hear about us?" field on demo request forms, and run programmatic content operations against a query map of 200 or more priority buyer questions. At this scale, the monitoring tool is table stakes. Our guide to winning AI search covers the enterprise content operations model in detail.
The fastest path to initial citation signal at the lowest software cost is Peec Starter at reportedly €89/month, paired with the free AEO content evaluator. Run the evaluator against your five highest-traffic content assets. Identify the extractability gaps. Restructure those pieces using answer-first sections of 40 to 60 words. Initial citation signals typically appear within one to two weeks of republishing, giving you concrete data before committing to a larger execution budget.
Every tool evaluated here shares one critical limitation: they measure the problem. None of them fix it. Citation rate tracking, share of voice dashboards, and competitive benchmarking are valuable inputs, but they do not produce a single line of optimized content, implement a single schema tag, or place a single accurate brand mention on a third-party source. This is where most AI visibility programs stall. For more on why standard SEO agencies struggle to close this gap, see our analysis of AEO expertise for B2B SaaS.
Quantifying AI-driven pipeline uncertainty
Attribution across AI search channels is genuinely hard, and any vendor who tells you otherwise is not being straight with you. Google Analytics 4 (GA4), HubSpot, and Salesforce often give you different numbers for the same quarter because they measure different things. AI-referred sessions often appear as direct traffic in GA4 because LLM interfaces do not pass referrer headers consistently. The practical fix is a layered attribution approach: UTM tags on all trackable AI-referred links, a "how did you hear about us?" field on every demo request form, and a monthly reconciliation between self-reported data and platform attribution. This produces a defensible board slide, not a perfect one. We cover the full attribution setup in our B2B SaaS SEO case studies piece.
"I have recommended you to multiple peer CMOs. There are large organizations like Hubspot and Ramp who have dedicated teams to work on large projects like AEO. For everyone else (except my competitors) there's Discovered Labs!" - Tom Wentworth, incident.io case study
Optimizing content for AI citations
The execution layer that turns monitoring data into citation rate lift is the CITABLE framework. Each component addresses a specific requirement of LLM passage retrieval:
- C - Clear entity and structure: A 2 to 3 sentence bottom-line-up-front (BLUF) opening that states the answer before any supporting detail.
- I - Intent architecture: Covers the main query and the adjacent questions a buyer will have in the same session.
- T - Third-party validation: Wikipedia entries, independent reviews, community signals, and news citations that LLMs use to verify claims.
- A - Answer grounding: Verifiable facts with source links, not unsourced assertions.
- B - Block-structured for RAG: Sections structured for retrieval-augmented generation, with tables, FAQs, and ordered lists as extractable units.
- L - Latest and consistent: Timestamps, updated facts, and unified claims across all published content.
- E - Entity graph and schema: Explicit relationships stated in copy, backed by structured data markup. The reason this framework moves citation rates is grounded in retrieval research. Karpukhin et al. showed that dense passage retrieval outperforms traditional sparse retrieval (BM25) by 9 to 19 points on top-20 passage tasks. Structure and semantic density matter more than keyword frequency for LLM citation. Answer early, keep sections focused, and make every passage independently quotable.
Addressing CMO concerns on AI search strategy
The three concerns that come up most often in discovery calls are board pressure to show AI search ROI, sunk cost anxiety about existing content investments, and skepticism about whether a new agency partner will actually move the metrics. Each has a concrete answer. Most existing content assets can be restructured for extractability rather than replaced. You are not starting over. You are restructuring for a different retrieval mechanism.
Expected time to first AI visibility
A realistic 90-day timeline for a B2B SaaS team starting from baseline:
- Week 2: Initial citations may begin appearing from newly published or restructured CITABLE content.
- Month 2: Structural work is in place and monitoring tools may show early citation movement on priority queries.
- Month 3: Measurable citation rate lift on priority clusters may emerge, with early AI-referred pipeline attribution accumulating in CRM. Full optimization across all three surface areas typically takes four to six months. These are observed outcomes from our client work, not guarantees.
Workflow automation for Salesforce
Setting up pipeline tracking for AI-referred leads inside Salesforce requires three components: a UTM source field that captures "ai-search" as a distinct channel, a hidden form field on your demo request page that auto-populates from that UTM, and a custom opportunity field that carries source attribution through to closed-won. Add a self-reported "how did you hear about us?" dropdown alongside the hidden field to catch AI-influenced leads who navigated directly to your site after an LLM recommendation. HubSpot users can replicate this with the same field architecture inside workflows and contact properties.
Flexible billing versus annual lock-ins
The monitoring platforms reviewed here all tie any material discount to annual billing commitments. AI search platforms are evolving quickly enough that a 12-month software commitment carries real optionality risk. Our retainer packages are month-to-month with no long-term commitment. Starter runs €6,995/month. If we stop delivering measurable citation rate lift, you can stop the engagement. That accountability mechanism is intentional.
Measuring AI visibility in 90 days
A 90-day board-ready measurement plan should cover four metrics:
- Citation rate at baseline and at 90 days across your highest-priority buyer queries.
- Share of voice versus your top competitors on those same queries.
- AI-referred sessions tagged via UTM, reconciled against self-reported attribution monthly.
- AI-referred Marketing Qualified Leads (MQLs) tracked as a distinct pipeline source in your CRM with a clear note on attribution methodology. Present caveats alongside the numbers. A board that understands the uncertainty in the data is easier to hold than one that gets precise-looking figures that break under scrutiny.
Conclusion
The four platforms in this guide each solve a real diagnostic problem. Peec is the lowest-friction starting point for small teams that need commercial query tracking and unlimited seats. Profound suits enterprise teams that need multi-LLM coverage breadth in a single dashboard. Scrunch adds configurable multi-engine monitoring for agencies or teams managing multiple brands. Trysight is worth evaluating if Google AI Overview monitoring is your primary concern and you are comfortable scoping pricing directly with the vendor. None of them close the gap on their own. Citation rate lift requires structured content built for passage retrieval, consistent claims across independent sources, and schema that makes your entity relationships explicit. If you want to map your current citation rate against your priority query set before committing to a tool or execution partner, our free AEO content evaluator and AI visibility audit produce the baseline you need to start the clock. Book a call and we'll tell you honestly whether we're a fit.
FAQs
What is the entry-level cost for these tools?
Entry-level pricing reportedly starts at approximately €89/month for Peec AI's Starter plan, around $99/month for Profound's Starter plan, and approximately $250/month for Scrunch's Core plan. Trysight's pricing requires a direct sales conversation to scope.
How long does it take to see initial citation data?
Peec and Profound reportedly populate baseline visibility reports within 24 to 48 hours of connecting your domain. Initial citation signals from newly optimized content typically appear within one to two weeks of indexing.
The four platforms evaluated reportedly do not offer deep native Salesforce or HubSpot connectors out of the box based on available information. Profound and Peec reportedly offer API access for custom builds (Peec's is in beta). Scrunch and Trysight reportedly require Zapier or developer-built API setups.
How is a citation rate different from a search ranking?
A citation rate measures how often your brand appears in LLM-generated answers for a defined set of queries, while a search ranking measures your URL's position in a traditional results page. Research we track from Ahrefs shows only 38% of cited pages also rank in the top 10 for the same query, down from 76% in mid-2025.
Can I improve my citation rate without replacing existing content?
Yes. Most existing content assets can be restructured for extractability rather than replaced. The CITABLE framework applies to published pages through targeted edits: restructuring sections to answer-first format, adding schema markup, and updating third-party validation signals across independent sources.
Key terms glossary
Citation rate: The percentage of times an LLM cites your brand when answering queries within your software category. It is the primary metric AI visibility tools track and the leading indicator of AI-sourced pipeline.
Information consistency: The alignment of product claims, pricing, and features across independent web sources including your site, Reddit, review platforms, and industry publications. LLMs reward claims that appear consistently across multiple independent sources, making off-page consistency a core ranking signal for AI retrieval.
Extractability: A content design standard that structures text into short, answer-first blocks so retrieval-augmented generation (RAG) systems can easily retrieve and quote them. Research by Karpukhin et al. showed that dense retrieval systems outperform sparse keyword-based retrieval by 9 to 19 points on top-20 passage tasks.
AEO Score: A metric that measures how effectively your content answers specific user queries compared to your competitors, aggregated across the LLMs you track. It combines citation rate, mention quality, and competitive share of voice into a single benchmark figure.
Share of voice: Your brand's citation count expressed as a percentage of total citations across your competitive set for a defined query pool. It is the AI-era equivalent of organic search market share and the most practical metric for board-level reporting on AI search progress.
